63 research outputs found
Lower body design of the âiCubâ a human-baby like crawling robot
The development of robotic cognition and a greater understanding of human cognition form two of the current greatest challenges of science. Within the RobotCub project the goal is the development of an embodied robotic child (iCub) with the physical and ultimately cognitive abilities of a 2frac12 year old human baby. The ultimate goal of this project is to provide the cognition research community with an open human like platform for understanding of cognitive systems through the study of cognitive development. In this paper the design of the mechanisms adopted for lower body and particularly for the leg and the waist are outlined. This is accompanied by discussion on the actuator group realisation in order to meet the torque requirements while achieving the dimensional and weight specifications. Estimated performance measures of the iCub are presented
A haptic-enabled multimodal interface for the planning of hip arthroplasty
Multimodal environments help fuse a diverse range of sensory modalities, which is particularly important when integrating the complex data involved in surgical preoperative planning. The authors apply a multimodal interface for preoperative planning of hip arthroplasty with a user interface that integrates immersive stereo displays and haptic modalities. This article overviews this multimodal application framework and discusses the benefits of incorporating the haptic modality in this area
Human-Like Impedance and Minimum Effort Control for Natural and Efficient Manipulation
Humans incorporate and switch between learnt
neuromotor strategies while performing complex tasks. Towards
this purpose, kinematic redundancy is exploited in order
to achieve optimized performance. Inspired by the superior
motor skills of humans, in this paper, we investigate a combined
free motion and interaction controller in a certain class of
robotic manipulation. In this bimodal controller, kinematic
degrees of redundancy are adapted according to task-suitable
dynamic costs. The proposed algorithm attributes high priority
to minimum-effort controller while performing point to
point free space movements. Once the robot comes in contact
with the environment, the Tele-Impedance, common mode
and configuration dependent stiffness (CMS-CDS) controller
will replicate the humanâs estimated endpoint stiffness and
measured equilibrium position profiles in the slave robotic
arm, in real-time. Results of the proposed controller in contact
with the environment are compared with the ones derived
from Tele-Impedance implemented using torque based classical
Cartesian stiffness control. The minimum-effort and interaction
performance achieved highlights the possibility of adopting
human-like and sophisticated strategies in humanoid robots or
the ones with adequate degrees of redundancy, in order to
accomplish tasks in a certain class of robotic manipulatio
Detecting Object Affordances with Convolutional Neural Networks
We present a novel and real-time method to detect
object affordances from RGB-D images. Our method trains
a deep Convolutional Neural Network (CNN) to learn deep
features from the input data in an end-to-end manner. The CNN
has an encoder-decoder architecture in order to obtain smooth
label predictions. The input data are represented as multiple
modalities to let the network learn the features more effectively.
Our method sets a new benchmark on detecting object affordances, improving the accuracy by 20% in comparison with
the state-of-the-art methods that use hand-designed geometric
features. Furthermore, we apply our detection method on a
full-size humanoid robot (WALK-MAN) to demonstrate that
the robot is able to perform grasps after efficiently detecting
the object affordances
Outlier-Robust State Estimation for Humanoid Robots*
Contemporary humanoids are equipped with visual and LiDAR sensors that are effectively utilized for Visual Odometry (VO) and LiDAR Odometry (LO). Unfortunately, such measurements commonly suffer from outliers in a dynamic environment, since frequently it is assumed that only the robot is in motion and the world is static. To this end, robust state estimation schemes are mandatory in order for humanoids to symbiotically co-exist with humans in their daily dynamic environments. In this article, the robust Gaussian Error-State Kalman Filter for humanoid robot locomotion is presented. The introduced method automatically detects and rejects outliers without relying on any prior knowledge on measurement distributions or finely tuned thresholds. Subsequently, the proposed method is quantitatively and qualitatively assessed in realistic conditions with the full-size humanoid robot WALK-MAN v2.0 and the mini-size humanoid robot NAO to demonstrate its accuracy and robustness when outlier VOLO measurements are present. Finally, in order to reinforce further research endeavours, our implementation is released as an open-source ROS/C++package
Ctrl-MORE: A Framework to Integrate Controllers of Multi-DoF Robot for Developers and Users
In recent years, many different feedback controllers for robotic applications have been proposed and implemented. However, the high coupling between the different software modules made their integration into one common architecture difficult. Consequently, this has hindered the ability of a user to employ the different controllers into a single, general and modular framework. To address this problem, we present Ctrl-MORE, a software architecture developed to fill the gap between control developers and other users in robotic applications. On one hand, Ctrl-MORE aims to provide developers with an opportunity to integrate easily and share their controllers with other roboticists working in different areas. For example, manipulation, locomotion, vision and so on. On the other hand, it provides to end-users a tool to apply the additional control strategies that guarantee the execution of desired behaviors in a transparent, yet efficient way. The proposed control architecture allows an easier integration of general purpose feedback controllers, such as stabilizers, with higher control layers such as trajectory planners, increasing the robustness of the overall system
Variable Configuration Planner for Legged-Rolling Obstacle Negotiation Locomotion: Application on the CENTAURO Robot
Hybrid legged-wheeled robots are able to adapt their leg configuration and height to vary their footprint polygons and go over obstacles or traverse narrow spaces. In this paper, we present a variable configuration wheeled motion planner based on the A* algorithm. It takes advantage of the agility of hybrid wheeled-legged robots and plans paths over low-lying obstacles and in narrow spaces. By imposing a symmetry on the robot polygon, the computed plans lie in a low-dimensional search space that provides the robot with configurations to safely negotiate obstacles by expanding or shrinking its footprint polygon. The introduced autonomous planner is demonstrated using simulations and real-world experiments with the CENTAURO robot
Teleimpedance Control of a Synergy-Driven Anthropomorphic Hand
In this paper, a novel synergy driven teleimpedance
controller for the PisaâIIT SoftHand is presented. Towards
the development of an efficient, robust, and low-cost hand
prothesis, the PisaâIIT SoftHand is built on the motor control
principle of synergies, through which the immense complexity
of the hand is simplified into distinct motor patterns. As the
SoftHand grasps, it follows a synergistic path with built-in
flexibility to allow grasping of objects of various shapes using
only a single motor. In this work, the hand grasping motion
is regulated with an impedance controller which incorporates
the userâs postural and stiffness synergy profiles in realtime.
In addition, a disturbance observer is realized which estimates
the grasping contact force. The estimated force is then fedback
to the user via a vibration motor. Grasp robustness and
transparency improvements were evaluated on two healthy
subjects while grasping different objects. Implementation of
the proposed teleimpedance controller led to the execution of
stable grasps by controlling the grasping forces, via modulation
of hand compliance. In addition, utilization of the vibrotactile
feedback resulted in reduced physical load on the user. While
these results need to be validated with amputees, they provide
evidence that a low-cost, robust hand employing hardwarebased
synergies is a viable alternative to traditional myoelectric
prostheses
HERI II: A Robust and Flexible Robotic Hand based on Modular Finger design and Under Actuation Principles
This paper introduces the design of a novel under-actuated hand with highly integrated modular finger units, which can be easily reconfigured in terms of finger arrangement and number to account for the manipulation needs of different applications. Each finger module is powered by a single actuator through an under-actuated transmission and equipped with a sensory system for delicate and precise grasping, which includes absolute position measurements, contact pressure sensing at finger phalanxes and motor current readings. Finally, intrinsic elasticity integrated in the transmission system make the hand robust and adaptive to impacts when interacting with the objects and environment. This highly integrated hand (HERI II) was developed for the Centauro Robot to enable robust and resilient manipulation. A set of experiments demonstrating the hand's grasping performance were carried out and fully verified the design effectiveness of the proposed hand
Online impedance regulation techniques for compliant humanoid balancing
This paper presents three distinct techniques, aimed at the online active impedance regulation of compliant humanoid robots, which endeavours to induce a state of balance to the system once it has been perturbed. The presence of passive elastic elements in the drives powering this class of robots leads to under-actuation, thereby rendering the control of compliant robots an intricate task. Consequently, the impedance regulation procedures proposed in this paper directly account for these elastic elements. In order to acquire an indication of the robotâs state of balance in an online fashion, an energy (Lyapunov) function is introduced, whose sign then allows one to ascertain whether the robot is converging to or diverging from, a desired equilibrium position. Computing this functionâs time derivative unequivocally gives the energy-injecting nature of the active stiffness regulation, and reveals that active damping regulation has no bearing on the systemâs state of stability. Furthermore, the velocity margin notion is interpreted as a velocity value beyond which the systemâs balance might be jeopardized, or below which the robot will be guaranteed to remain stable. As a result, the unidirectional and bidirectional impedance optimization methods rely upon the use of bounds that have been defined based on the energy functionâs derivative, in addition to the velocity margin. Contrarily, the third techniqueâs functionality revolves solely around the use of Lyapunov Stability Margins (LSMs). A series of experiments carried out using the COmpliant huMANoid (COMAN), demonstrates the superior balancing results acquired when using the bidirectional scheme, as compared to utilizing the two alternative techniques
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